Custom LLMs for Internal Enterprise Data: Build or Buy?
Datahub Analytics Team
The real question is not build or buy, but what problem are we solving - and how critical is AI to solving it? …
Prompt Engineering for BI: A New Skill for Data Teams
As AI reshapes how organizations interact with data, prompt engineering is emerging as a foundational skill for modern data teams. It determines how effectively AI translates data into insight - and how safely and accurately those insights are used. …
AI-Generated Insights: Can Machines Replace Analysts?
The future belongs to organizations that combine the power of AI with human expertise—where machines surface what matters, and analysts decide what to do about it. …
The Future of Interactive Data Stories: BI Meets UX Design
The future of BI is not just about visualizing data - it’s about communicating insight. Interactive data stories represent this new direction. They combine the power of analytics with the clarity of UX design, transforming dashboards into meaningful, memorable experiences that drive action. …
Data Trust Scores: The New Metric for Enterprise Analytics
Enterprises that adopt trust scoring will make smarter decisions, reduce risk, and enhance confidence across every data-driven initiative. Those that ignore it may find themselves navigating blind in an increasingly complex and regulated data landscape. …
Serverless Analytics: The Future of Scalable Insights
As data volumes continue to grow and business demands evolve, serverless analytics will stand at the center of the modern data ecosystem—powering the next generation of real-time, predictive, and AI-driven insights. …
Winning in the Experience Economy with Real-Time Customer Analytics
Companies that embrace real-time intelligence will lead their industries. Those that rely on outdated data or static insights will increasingly fall behind. …
From Insights to Outcomes: Closing the Last Mile in Analytics
The analytics journey is not complete when a dashboard is delivered. It is complete when a decision changes. Analytics maturity is defined by whether an organization can consistently and predictably translate insights into outcomes. That is the last mile - messy, human, operational, and extremely …
Telemetry for Data Pipelines: Observability Isn’t Just for Infra
As organizations scale analytics and AI initiatives - especially in ambitious markets like KSA - visibility into data flow, quality, and reliability becomes essential. Telemetry is not a luxury. It is the foundation of trustworthy insight. …
Natural Language Dashboards: Is This the End of Filters?
Dashboards have evolved from simple reports to dense analytical canvases. With dozens of filters - time, segments, categories, countries, channels - users get overwhelmed before they even start analyzing. …
